Posts

Historically, software-as-a-service (SaaS) has been built on databases with structured data, as you might find in an Excel spreadsheet.

But the ability of large language models to extract insights from unstructured information changes this architecture : data repositories like data lakes are becoming essential parts of modern SaaS stacks.

For example, next generation marketing software might ingest structured data from Google Analytics and lead capture software as before. But it also might start to analyze customers’ behavior in online webinars, conversations with customer support and sales teams, and more - all of which is unstructured data stored in video & raw text & audio files.

Open source software has evolved from a movement into a big business. Gitlab, Confluent, Databricks, MongoDB, Elastic, Snyk, Hashicorp, Mulesoft, have become huge companies using open source software.

Across, just these they have generated about $100b in market cap.

Early on, open source pioneers viewed software freedom as an end unto itself. They encouraged permissive licensing so anyone could use, modify, and distribute their code.

Large-language models have transformed how millions interact with products : from customer support to code generation to legal document analysis.

These new engagement models invite users through a meaningfully different product journey. Every application now speaks English. Understanding user behavior is essential to building great products.

How should a product manager gauge the customer experience? Lots of new challenges arise :

• First, the models are non-deterministic which means sometimes the same question phrased a bit differently produces unexpected results.
• Second, users can input anything they like which can lead to model drift & unexpected outputs potentially including might include PII (personally identifiable information) or other sensitive content.
• Third, how should a PM think about the user journey through a conversation? What is a successful interaction? It’s no longer straightforward clicking through five screens.

Context.ai is a product analytics platform for LLM-powered applications. Context.ai gives product builders an understanding of how users engage with their products and visibility into product performance, so they can build better experiences.

On Friday, Klaviyo filed their S-1 - one of the first software companies to do so since the beginning of the economic downturn in late 2022. It’s a marvel of business with strong growth, great efficiency & the potential to reignite interest in SaaS IPOs.

Klaviyo is a customer data platform company which ingests marketing data & empowers their customers to automate their marketing with data & AI. The company raised $455m in venture capital, but has used only $15m, growing nearly entirely on profits.

Aakash Gupta published Product Requirements Documents (PRDs): A Modern Guide.

credit: Wired

There at the top of the post is a photo of the 2007 APM class, coincidentally my class (I’m there on the right), departing San Francisco for a trip that would take us to Tokyo, Beijing, Bangkok, Bangalore & Tel Aviv. Steven Levy, a reporter, traveled with us & documented the trip.

If we needed another exclamation point on the tremendous growth opportunity, NVidia’s earnings punctuated the euphoria with gusto.

Revenue grew 88% in a quarter, nearly doubling. The company had projected $11b & exceeded projections by $2.5b or 26%.

The Data Center business, which sells to clouds & consumer companies drove nearly all of the growth, surging 141% quarter-over-quarter to $10.3b & nearly 3x annually.

The post-Covid surge in venture capital pushed valuations & activity to record breaking heights. Since then, investing activity dropped precipitously.

When a collapse follows a surge, mean reversion suggests behavior should revert to a reasonable baseline. By building a linear model we can hazard a guess of when that might be.

The blue line shows actual Seed, A, & B activity by quarter. The red is a linear model based on data from 2010 to 2018 that predicts activity rates for each financing series of US & Canadian software companies. [1]

The Fed no longer predicts a recession. Economic data is turning more positive, eg housing starts exceeded forecasts. 80% of public companies beating earnings estimates - three percentage points higher than the 5 year average.

On the other hand, enough uncertainty permeates the market to depress prices. Public software companies’ share prices have fallen 10% on average in the last 30 days dotted with positive notes like the stabilization cloud growth rates for Microsoft & Google.

Classical computer programming is about rules. What does a program do when a user inputs 4 + 7? Or a credit card number? Or clicks on a button. I spent years as a programmer translating human rules into rules the computer would understand.

One day, with enough experience, the number of exceptions to rules dawns on a programmer. It’s no different than english.

Imagine writing a text-to-speech program :

Home & tome rhyme. So does comb. But not come.

Carta released their state of the market report. A few data points stood out to me

Downrounds constitute 20% of all rounds, up 2x from historical norms. Bridge rounds account for 38% of all rounds in Q2. Cooley reported 2.9% of rounds recapitalized the company. Recaps effectively delete the previous cap table & start a new one.

These are all signs of the harder fundraising times.

The glass-half full view is that 80% of all rounds are flat or up.